1. Technical Field
The present disclosure relates to image registration and, more specifically, to methods and systems for the registration of medical images.
2. Discussion of the Related Art
Medical imaging is the field of visualizing an internal structure of a patient subject using an image acquisition device. Examples of image acquisition devices include Computed Tomography (CT) scanners, Magnetic Resonance (MR) scanners, Positron Emission Tomography (PET) scanners, Single Positron Emission Tomography (SPECT) scanners and ultrasound scanners.
Medical imaging data may be acquired, for example, by scanning the subject with one of the above-named scanners. The acquired data may then be processed using computers to generate a visual representation of the internal structure of the subject. For example, in CT scanning, imaging data is rendered into a set of two-dimensional slices that are combined to form a three-dimensional visualization of the internal structure.
Medical images may be used by a practitioner, for example a radiologist, to identify injury and disease in the subject. To this end, it is often beneficial to combine imaging data from two or more medical images to add to the total amount of information that may be represented in the visual representation. The process of combining imaging data from multiple medical images is known as image registration. Image registration may thus be used to combine multiple images acquired from the same scanner, for example, to combine two CT images, or to combine multiple images from different scanners, for example, a CT image and an MR image.
Image registration of images may be used to align multiple images such that after registration, an overlay of the multiple images may show the same anatomical structure at the same position.
Rigid registration has been developed as a technique for registering two discrete images. In rigid registration, a transformation function may be applied to a first image known as the deformed image to map the deformed image onto a second image known as reference image. In rigid registration, the transformation function may be a linear matrix that is multiplied by each pixel of the deformed image to map that pixel onto the reference image. Thus, the transformation function is used to provide output coordinates for a given set of input coordinates.
Image registration is complicated by the fact that structures within the human body may change shape and relative location as a result of either normal conditions or as disease progresses. Under such conditions, rigid image registration may not be able to effectively map the deformed image to the reference image. Accordingly, techniques for non-rigid registration have been developed. In non-rigid registration, a non-rigid transformation model is used to map the deformed image to the reference image in a way that allows for and corrects for distortion of the internal structure of the subject in a non-linear manner.
While non-rigid transformation models have been developed, their implementation may be time consuming.